CN111612326A - Comprehensive evaluation method for power supply reliability of distribution transformer - Google Patents
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Abstract
The invention relates to a comprehensive assessment method of distribution transformer power supply reliability, which comprises the steps of acquiring data from a power utilization information acquisition system, mining an index capable of reflecting the distribution transformer power supply reliability from the data, wherein the index comprises 4 indexes of distribution transformer fault power failure, namely 4 indexes of continuous power failure times, short-time power failure times, average continuous power failure duration, average short-time power failure duration, and 6 indexes of distribution transformer fault risk, namely overload times, serious overload times, power factor abnormal times, three-phase unbalance times and low-voltage times; and (3) carrying out data processing on the 10 characteristics, and respectively carrying out modeling evaluation on the distribution transformer fault power-off index and the distribution transformer fault risk index by applying an AHP hierarchical analysis method and a TOPSIS algorithm to finally obtain a comprehensive evaluation value of the power supply reliability of the distribution transformer. The method can effectively diagnose the distribution transformer with poor reliability, assist power supply companies to develop key attention, active first-aid repair and other services, and support the planning and transformation scheme of the power distribution network.
Description
Technical Field
The invention belongs to the technical field of electric power big data application, and particularly relates to a comprehensive evaluation method for distribution transformer power supply reliability.
Background
The distribution transformer is huge in quantity, is the most important terminal equipment in the power system, can be in different operation states according to the actual load condition brought during the operation, and has great influence on the whole power industry due to the reliability.
For the analysis and evaluation of the reliability of the transformer, scholars at home and abroad put forward a large number of research methods and have good effects in engineering practice, Liuxing Heng et al realize the online diagnosis of the deformation fault of the distribution transformer winding through analysis, and LI L et al evaluate the running state of the transformer from the fault symptom to the whole body through a method of determining the weight by the association rule. In addition, some documents implement analysis and evaluation of transformer reliability based on an object theory, a cloud model, and the like.
However, these researches are based on specific parameter information of the transformer in electrical or mechanical aspects, and focus on evaluating the reliability of the transformer in safety, and the researches on the power supply reliability of the power grid during the actual operation of the transformer are still lacked.
Disclosure of Invention
The invention aims to provide a comprehensive assessment method for power supply reliability of a distribution transformer, which can effectively diagnose the distribution transformer with poor reliability, assist a power supply company to develop important attention, active first-aid repair and other services, and meanwhile, effectively support a planning and transformation scheme of a power distribution network based on an assessment result of the invention.
The invention adopts the following technical scheme:
a comprehensive evaluation method for distribution transformer power supply reliability comprises the following steps:
(1) acquiring distribution transformation archive data, load data, current data, voltage data, power factor data and fault power failure information data;
(2) calculating a distribution transformer fault power-off index and a distribution transformer fault risk index;
(3) processing the data obtained in the step (2);
(4) respectively calculating the weight coefficients of the distribution transformer fault power failure indexes and the distribution transformer fault risk indexes by using an AHP analytic hierarchy process;
(5) calculating the reliability evaluation value of the distribution transformer based on the distribution transformer fault outage class index and the distribution transformer fault risk class index by using a TOPSIS algorithm;
(6) and calculating a comprehensive evaluation value of the power supply reliability of the distribution transformer.
Further, in the step (1), data acquisition is performed from the electricity consumption information acquisition system according to a certain distribution type within a certain area range and a period to be analyzed.
Further, the distribution transformer fault power-off indexes in the step (2) include the number of continuous power-off times, the number of short-time power-off times, the average duration of power-off and the average duration of short-time power-off.
Further, the distribution transformer fault risk indexes comprise heavy load times, overload times, serious overload times, power factor abnormal times, three-phase unbalance times and low voltage times.
Further, the data processing in the step (3) includes setting a numerical upper limit and a numerical minimum value of each feature and performing normalization processing on each index.
Further, the step (4) specifically includes the following steps:
(A) respectively constructing a judgment matrix of a distribution transformer fault power failure index and a judgment matrix of a distribution transformer fault risk index;
(B) respectively carrying out consistency check on the two judgment matrixes;
(C) after consistency check is carried out on the two judgment matrixes, the weight coefficient of each index is calculated by respectively utilizing the eigenvector corresponding to the maximum characteristic root.
Further, the step (5) specifically includes the following steps:
(I) respectively constructing a characteristic matrix of a distribution transformer fault power failure index and a characteristic matrix of a distribution transformer fault risk index;
(II) weighting the characteristic matrix of the distribution transformer fault power failure type index and the characteristic matrix of the distribution transformer fault risk type index respectively to obtain weighted characteristic matrices;
(III) finding the optimal solution vector and the worst solution vector of each column;
(IV) calculating Euclidean distances between each index and the optimal solution vector and the worst solution vector;
and (V) respectively calculating the grading values of the distribution transformer fault power-off index and the distribution transformer fault risk index.
In the step (III), the value of each characteristic is zero and is used as an optimal solution; the numerical upper limit of each feature is taken as the worst solution.
Further, the step (6) specifically includes the following steps:
(a) calculating grading weights of the distribution transformer fault power failure indexes and the distribution transformer fault risk indexes by adopting a variable weight method;
(b) and weighting the score values and summing to obtain a distribution transformer power supply reliability comprehensive score value, wherein the smaller the score value is, the worse the distribution transformer power supply reliability is.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the above-mentioned method.
The invention has the beneficial effects that:
1. the distribution transformer power supply reliability can be evaluated by analyzing the distribution transformer data of the power utilization information acquisition system without additional equipment investment.
2. The distribution transformer with poor reliability can be found in time, service personnel can be reminded to pay attention to the distribution transformer, and power grid and user loss caused by passive maintenance is avoided, so that the current situation of emergency repair work is improved, and the distribution transformer has important practical significance and economic and social benefits.
3. The whole analysis process of the invention does not need manual intervention, saves a large amount of human resources and simultaneously improves the accuracy of the analysis result.
4. The invention extracts distribution transformer fault power failure index characteristics and distribution transformer fault risk index characteristics from a service perspective, and the two index characteristics accurately reflect the power supply reliability of the distribution transformer.
5. By analyzing the reliability assessment value based on the distribution transformer fault power failure index and the reliability assessment value based on the distribution transformer fault risk index, the comprehensive assessment result of the distribution transformer power supply reliability is obtained by adopting a variable weight weighting mode, the calculation process is clear, the result is reasonable and accurate, and the assessment result is more objective when the number of regional public variables is more.
6. The electricity consumption information acquisition system basically realizes full coverage and full acquisition, and the method has strong popularization.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
FIG. 2 is a block diagram of the index feature of the present invention.
Fig. 3 is a distribution diagram of the power supply reliability scores of the common variation in the area a in the calculation example.
FIG. 4 is a hardware implementation framework of the present invention.
Detailed Description
As shown in fig. 1, the invention provides a comprehensive assessment method for distribution transformer power supply reliability, which includes acquiring load data, voltage and current data, and power failure information of a specific type of distribution transformer in a certain area range in a period to be analyzed from an electricity information acquisition system, and mining indexes capable of reflecting distribution transformer power supply reliability from the load data, the voltage and current data, and the power failure information, including distribution transformer fault power failure indexes and distribution transformer fault risk indexes, wherein the distribution transformer fault power failure indexes specifically include 4 characteristics of continuous power failure times, short-time power failure times, average continuous power failure duration, and average short-time power failure duration. The distribution transformer fault risk indexes specifically comprise 6 characteristics of overload times, severe overload times, power factor abnormal times, three-phase unbalance times and low voltage times. And (3) carrying out data processing on the 10 characteristics, respectively giving weights to the distribution transformer fault power-off index and the distribution transformer fault risk index by applying an AHP hierarchical analysis method, and further respectively carrying out modeling evaluation on the distribution transformer fault power-off index and the distribution transformer fault risk index by applying a TOPSIS algorithm. And finally, weighting and summing the grading values of the distribution transformer fault power-off indexes and the grading values of the distribution transformer fault risk indexes in a variable weight mode to finally obtain a comprehensive evaluation value of the distribution transformer power supply reliability.
The above method is described in detail below with reference to the accompanying drawings and specific embodiments.
1. And (6) data acquisition.
The method comprises the steps of obtaining archive data, load data, current data, voltage data, power factor data and fault and power failure information data of distribution transformers in a power utilization information acquisition system according to a period to be analyzed and certain distribution transformer types (such as public distribution transformers or special distribution transformers) in a certain area range.
2. And calculating the distribution transformer fault power failure indexes and the distribution transformer fault risk indexes.
2.1 distribution transformer fault power-off index
As shown in fig. 2, the distribution transformer fault outage type indicators specifically include the number of continuous blackouts, the number of short-term blackouts, the average continuous blackout duration, and the average short-term blackout duration.
And defining the power failure with the fault power failure duration less than or equal to 3min as short-time power failure, and defining the power failure with the fault power failure duration more than 3min as continuous power failure.
Short-time power failure times: counting the number of short-time power failure generated by the accumulated distribution transformer in the period to be analyzed and recording as HtThe calculation formula is as follows:
in the formula (1), n is the number of days of the cycle to be analyzed, hiThe number of short-time power failure occurring every day in the period to be analyzed. The power supply reliability of the distribution transformer is poor as the number of short-time power failures increases.
PersistencePower failure times: counting the number of the continuous power failure generated by the distribution transformer in the period to be analyzed, and recording as LtThe calculation formula is as follows:
in the formula (2), n is the number of days of the cycle to be analyzed, liThe number of continuous power failure occurring every day in the period to be analyzed. The greater the number of continuous power failures, the worse the power supply reliability of the distribution transformer.
Average short-time power failure duration: counting the ratio of the total power failure duration and the number of power failures in a short time in a period to be analyzed, and recording as PtThe calculation formula is as follows:
in the formula (3), n is the number of days of the cycle to be analyzed, TiFor short-term power failure duration of each time of distribution transformer, HtThe number of power failure in short time. The larger the average short-time power failure duration value is, the worse the power supply reliability of the distribution transformer is.
Average duration of power outage: counting the ratio of the total duration of the continuous power failure of the distribution transformer to the number of the continuous power failure in the period to be analyzed, and recording as MtThe calculation formula is as follows:
in the formula (4), N is the number of days of the cycle to be analyzed, NiFor the duration of each continuous power failure of distribution transformer, LtFor the number of continuous power outages. The larger the average continuous power failure duration value is, the worse the power supply reliability of the distribution transformer is.
2.2 distribution transformer fault risk index
The distribution transformer is subjected to heavy overload, so that the service life of equipment is shortened, the distribution transformer is unstable in voltage, frequent in fault and the like, potential fault hazards and operation risks are brought to a power grid, and even huge economic loss is brought. Therefore, the heavy overload condition of the distribution transformer is necessary to be used as an index feature of the fault risk class of the distribution transformer.
The low-voltage phenomenon of the power distribution network not only influences the production and life of users, but also increases the line loss of a power distribution network line and the economic operation of a transformer, so that electric equipment is damaged, and meanwhile, the voltage quality is also an important index for evaluating the electric energy quality. Therefore, it is necessary to use the low voltage condition of the distribution transformer as the distribution transformer fault risk index characteristic.
When the distribution transformer operates under the condition of unbalanced three-phase load, zero-sequence current is generated and changes along with the unbalanced degree of the three-phase load, and the larger the unbalanced degree is, the larger the zero-sequence current is. The distribution transformer can accelerate aging due to overheating of zero sequence current, and the service life of equipment is shortened. Meanwhile, the loss of the distribution transformer is increased due to the existence of the zero sequence current. Therefore, the three-phase unbalance condition of the distribution transformer is necessary to be used as the distribution transformer fault risk index characteristic.
The reduction of the power factor leads to the increase of current, so that the active power output by the distribution transformer is reduced, and the capacity of equipment cannot be fully utilized; meanwhile, the electric energy loss and the section of the lead are increased, the voltage loss is increased, and the power factor directly influences the full utilization of power generation, supply and utilization equipment of the power system. Therefore, the abnormal power factor condition of the distribution transformer is necessary to be used as the index feature of the distribution transformer fault risk class.
The distribution transformer power failure event is not caused by the abnormal operation conditions such as heavy overload, low voltage, three-phase imbalance and the like of the distribution transformer, but the power supply reliability of the distribution transformer is also influenced. Therefore, 6 characteristics of the heavy load times, the overload times, the severe overload times, the three-phase unbalance times, the power factor abnormal times and the low voltage times are extracted to serve as distribution transformer fault risk indexes.
The operation load data of the distribution transformer is mainly collected for 1 time at an integral point every day, and the total time is 24 times in a day. Then the per acquisition point loading rate riThe formula (5) is shown in the following formula.
In the formula (5), piFor two of each acquisition pointAnd the secondary active load value is CT (current transformer) transformation ratio of the distribution transformer, PT (potential transformer) transformation ratio of the distribution transformer and CAP (capacity of operation) of the distribution transformer.
Number of severe overloads: load rate r of distribution and transformation acquisition pointiMore than or equal to 100 percent, and the standard of lasting 7 acquisition points and above is defined as the serious overload phenomenon of the distribution transformer, and the times of the serious overload of the distribution transformer in the period to be analyzed is counted and recorded as CseriousThe calculation formula is as follows:
in the formula (6), n is the number of days of the cycle to be analyzed, cseriousIndicating the number of times of severe overload phenomenon of distribution transformer per day. The greater the number of severe overloads, the worse the power supply reliability of the distribution transformer.
Overload times are as follows: load rate r of distribution and transformation acquisition pointiMore than or equal to 100 percent, continuously collecting 3 or more collection points, defining the collection points as the overload phenomenon of the distribution transformer when the collection points do not reach the standard of the serious overload phenomenon, counting the overload times of the distribution transformer in the period to be analyzed, and recording the number as CoverThe calculation formula is as follows:
in the formula (7), coverIndicating the number of times of daily overload phenomena of the distribution transformer.
The number of times of overloading: load rate r of acquisition point by distribution transformeriMore than or equal to 80 percent, continuously collecting 3 or more collection points, but not reaching the overload and serious overload phenomena, defining the overload phenomena of the distribution transformer, counting the times of the overload phenomena of the distribution transformer in the period to be analyzed, and marking as CheavyThe calculation formula is as follows:
in the formula (8), n is the number of days of the cycle to be analyzed, cheavyIndicating daily occurrence of heavy load phenomenon of distribution transformerThe number of times. The more the number of overloads, the worse the power supply reliability of the distribution transformer.
Three-phase unbalance times: the distribution current data were collected 1 time per day at full points for a total of 24 times a day. The three-phase imbalance rate I of each collection pointiThe calculation formula is
In the formula (9), ImaxIs the maximum value of the current in the three phases of the collection point A, B, C, IminIs the current minimum in the three phases at this collection point A, B, C.
Three-phase unbalance rate I of distribution transformer acquisition pointiMore than 25%, and a load factor riMore than 60%, and the standard of 2 or more acquisition points is continued, the three-phase imbalance phenomenon is defined, the times of the three-phase imbalance of the distribution transformer in the period to be analyzed are counted and marked as Ibalance
In the formula (10), n is the number of days of the cycle to be analyzed, ibalIndicating the number of times of three-phase imbalance of the distribution transformation per day. The more the three-phase unbalance times, the worse the power supply reliability of the distribution transformer.
Number of power factor abnormality times: firstly, the average power factor of the distribution transformer in the period to be analyzed in each day is calculated, and the average power factor can be obtained according to the formula (11):
in formula (11), QtFor distribution of the reactive power consumption on the day, EiThe active power consumption of the day is changed.
Power factor f of day if distribution and transformationpAnd if the power factor is less than 85%, the power factor abnormality of the distribution transformer is defined to occur on the day. Counting the times of power factor abnormality of distribution transformer in the period to be analyzed, and recording as Fanorm. Power factorThe larger the number of abnormal times, the worse the power supply reliability of the distribution transformer.
Low voltage times: the distribution transformer operating voltage data was collected 1 time a day at full points for a total of 24 times a day. Then a low voltage rate V per collection pointiThe calculation formula is as follows:
u in formula (12)220Is a standard voltage, Ui24 collection point voltages per day were distributed.
Low voltage ratio V of distribution transformeriGreater than 10%, and the current voltage UiMore than 150V, lasting 2 collection points and more, defining the low voltage phenomenon, counting the times of the low voltage phenomenon of the distribution transformer in the period to be analyzed, and marking as UyThe calculation formula is as follows:
in the formula (13), n is the running days of the distribution variation in the period to be analyzed, and u isyIndicating the number of times of low voltage abnormality per day of distribution transformation. The higher the number of times of low voltage, the worse the power supply reliability of the distribution transformer.
3. And processing the data.
3.1 set the numerical Upper bound of each feature
Taking the severe overload frequency characteristic as an example, according to business understanding, the severe overload frequency of the extreme distribution transformer reaches a very high value, when the TOPSIS algorithm is applied for modeling, the worst value of each characteristic of all evaluation objects needs to be found, and the very high severe overload frequency exceeds the worst condition considered in business because the extreme distribution transformer reaches the degree, so that a great influence is caused on an evaluation result, and therefore, an upper limit value of the severe overload frequency characteristic needs to be set.
The method for setting the upper limit value of the severe overload frequency characteristic comprises the following steps:
firstly, setting the number of all distribution transformers to be evaluated as m, and setting the proportion according to which the upper limit value is selectedIf 1%, calculating the quantity value m according to which the upper limit value is setsetE.g. mset＝1％×m。
Secondly, grouping all distribution transformers to be evaluated according to the times of serious overload occurrence in a period to be analyzed, searching the number of corresponding distribution transformers which is greater than or equal to m for the first time according to the number of times from large to smallsetThe number of severe overload is the upper limit value, and is recorded as cserious_max。
Finally, the serious overload times in all distribution transformers to be evaluated exceed an upper limit value cserious_maxUsing the maximum value cserious_maxAnd (6) replacing.
Similarly, the upper limit value of the other 9 characteristics is set according to the steps.
3.2 setting the minimum value of each feature
In the original TOPSIS comprehensive evaluation method, the optimal value of each characteristic of all evaluation objects needs to be found out to form an optimal solution F*However, the optimal solution is not an ideal solution, so that the optimal value of each feature is customized through business understanding to form the optimal solution. In the invention, one ideal distribution transformer is self-defined, and the value of each characteristic is set to be zero as the optimal solution.
3.3 maximum minimum normalization
In order to solve the problems of inconsistent characteristic dimensions and overlarge difference of the numerical levels of the characteristic items, the data are normalized by adopting maximum and minimum normalization.
In the formula (14), Cserious_iRepresenting the number of severe overloads before i-th distribution transformation normalization, Cserious_maxMaximum value, C, representing the number of severe overloads in all distribution variations to be evaluatedserious_minDenotes the minimum number of severe overloads, C'serious_iThe normalized value of the number of severe overload times of the i-th station distribution transformer is shown.
Similarly, the other 9 features are normalized according to equation (14).
4. And respectively calculating the weight coefficients of the distribution transformer fault power failure indexes and the distribution transformer fault risk indexes by using an AHP analytic hierarchy process.
4.1AHP analytic hierarchy process principle:
(1) constructing a decision matrix
And n evaluation features are set, and the evaluation features are compared pairwise to obtain a judgment matrix B.
Element B of BijAnd (4) representing the importance comparison result of the ith evaluation characteristic and the jth evaluation characteristic, wherein the comparison result is usually marked by a 1-9 scale method. As shown in table 1.
TABLE 1
| Scale | Means of |
| 1 | Showing the same importance of the two factors compared |
| 3 | Indicating that the former is slightly more important than the latter |
| 5 | Indicating that the former is significantly more important than the latter in comparison with two factors |
| 7 | Indicating that the former is more important than the latter |
| 9 | Indicating that the former is extremely important compared to the latter |
| 2,4,6,8 | Intermediate value representing the above-mentioned adjacent judgment |
(2) Consistency check
Since the judgment matrix B is influenced by the subjective judgment of the decision maker, a certain error inevitably exists, consistency check must be performed, and the consistency ratio CR is defined as shown in formula (16).
In the formula (16), CI is a consistency index, which can be obtained as in the formula (17); RI is an average random consistency index, and its values are shown in table 1.
In equation (17), λ max is the maximum feature root of the determination matrix B.
TABLE 2RI values
And when CR <0.1, judging that the consistency of the matrix meets the requirement, and otherwise, reconstructing the judgment matrix.
(3) Weight coefficient calculation
After the consistency check is performed on the judgment matrix B, the eigenvector U corresponding to the maximum characteristic root λ max is shown as formula (18), and the weight coefficient of each evaluation index can be obtained by formula (19).
U＝[u1,u2,…,un]T(18)
In the formula (18), wiIs a weight coefficient of the i-th evaluation index, uiIs the ith element in W. The matrix composed of the evaluation index weight coefficients is denoted as W, and is shown as formula (20).
4.2 weight setting of distribution transformer fault power-off index
4 characteristics of average short-time power failure times, average continuous power failure times, average short-time power failure duration and average continuous power failure duration are constructed to form a judgment matrix B1Solving each feature weight according to equations (16) to (20) with a weight matrix of
4.3 distribution transformer fault risk class indicator weight setting
6 characteristics of serious overload times, heavy load times, three-phase unbalance times, power factor abnormal times and low voltage times are constructed to form a judgment matrix B2Solving each feature weight according to the formula (16) to the formula (20), wherein the weight matrix is
5. And calculating the reliability evaluation value of the distribution transformer based on the distribution transformer fault outage class index and the distribution transformer fault risk class index by using a TOPSIS algorithm.
And (3) evaluating and analyzing the distribution transformer fault power failure indexes and the distribution transformer fault risk indexes respectively by applying a TOPSIS (technique for order preference by similarity to similarity. The method comprises the following specific steps:
5.1TOPSIS comprehensive evaluation method principle
The TOPSIS comprehensive evaluation method is a common effective method in multi-target decision analysis, has wide application in a plurality of fields, carries out sequencing according to the distance between a limited number of evaluation objects and an optimal solution and a worst solution, and is optimal if the evaluation objects are closest to the optimal solution and are also farthest from the worst solution; otherwise, the solution is not optimal, wherein each index value of the optimal solution reaches the optimal value of each evaluation index, and each index value of the worst solution reaches the worst value of each evaluation index. The specific principle is as follows.
Let the evaluation object have n index features, and the feature set is z ═ z1,z2,…znAnd constructing and forming a feature matrix Z, as shown in formula (21):
in the formula (21), m represents the number of evaluation targets.
Solving the weight coefficient of each characteristic by using an AHP hierarchical analysis method to obtain a weight matrix W as shown in a formula (22),
the weighted feature matrix Z' is calculated as shown in equation (23),
find the best value of each column as fj *Forming an optimal solution F*＝[f1 *,f2 *,···fn *]Finding the worst value of each column and recording as fj ^Forming the worst solution F^＝[f1 ^,f2 ^,···fn ^]。
Calculating index characteristics and optimal solution F of each evaluation object*And the worst solution F^The euclidean distance of (c). As shown in formula (24)) As shown in (a) to (25),
finally, calculating the score value X of the evaluation object, as shown in the formula (26),
X＝Si^/Si*+Si^(26)
the higher the score value X, the more excellent the evaluation object.
5.2 improved TOPSIS comprehensive evaluation method
In the original TOPSIS comprehensive evaluation method, the optimal value of each feature of all evaluation objects needs to be found out to form an optimal solution and a worst solution. The optimal solution and the worst solution are probably not ideal solutions, so that the optimal solution is formed by preprocessing data and customizing the ideal solution of each feature through business understanding. In the invention, the original TOPSIS is improved, an ideal distribution transformer is defined by a user, and the value of each characteristic is set to be zero as an optimal solution. And setting a numerical upper limit for each feature, and taking the numerical upper limit of each feature as the worst solution.
5.3 distribution transformer power supply reliability evaluation based on distribution transformer fault power failure indexes
4 characteristics of average short-time power failure times, average continuous power failure times, average short-time power failure duration and average continuous power failure duration are constructed to form a matrix Z1Combining the above distribution transformer fault power-off index weight W1Calculating distribution transformer power supply reliability rating value X based on distribution transformer fault power stop indexes according to formulas (21) to (26)1Value of credit X1The smaller the size, the worse the reliability of the distribution.
5.4 distribution transformer power supply reliability evaluation analysis based on distribution transformer fault power failure indexes
6 characteristics of serious overload times, heavy load times, three-phase unbalance times, power factor abnormal times and low voltage times are constructed to form a matrix Z2Combining the above distribution transformer fault power-off index weight W2Obtaining a distribution transformer power supply reliability rating value X based on distribution transformer fault risk indexes according to formulas (21) to (26)2Value of credit X2The smaller the size, the worse the reliability of the distribution.
6. And calculating a comprehensive evaluation value of the power supply reliability of the distribution transformer.
Distribution transformer power supply reliability rating value X based on distribution transformer fault power-off index1And a distribution transformer power supply reliability rating value X based on distribution transformer fault risk indexes2Both the grading values are considered as being equally important because the power supply reliability of the distribution transformer can be reflected, but when a certain type of state of the distribution transformer is in a serious condition, the evaluation result of the final power supply reliability of the distribution transformer is not greatly influenced by the method, which is obviously inconsistent with the reality, so that the grading weights w' are calculated by adopting a variable weight method after comprehensive consideration, and the formula is shown as (27) and (28).
W 'of'1Is a score value X1Corresponding weight, w'2Is a score value X2The corresponding weight. The calculation formula (29) of the distribution transformer power supply reliability comprehensive rating value X shows that the distribution transformer power supply reliability is worse when the rating value X is smaller.
Example of calculation
In order to verify the reasonableness of the analysis and evaluation, the information data of the public transformer, load, voltage, current and power failure of the area A in 2018 of the Hebei south network are analyzed, and 64431 public transformers are calculated.
Constructing a public transformation fault and power failure index according to formulas (1) to (4), constructing a public transformation fault risk index according to formulas (5) to (13), sorting all feature data of the public transformation to be evaluated in the area A, setting an upper limit value of each feature according to a data processing rule, and simultaneously customizing an ideal public transformation, wherein each feature value is zero. And (4) carrying out maximum and minimum normalization processing on each characteristic, and respectively setting weights for the public transformation fault power-off index characteristic and the public transformation fault risk index characteristic by using formulas (15) - (20). And (4) according to the characteristic item matrix and the weight matrix, applying formulas (21) to (26) to respectively carry out reliability evaluation on the distribution transformer fault power failure index characteristic and the public transformer fault risk index characteristic. Finally, the overall evaluation value of the common variable reliability is calculated using equations (27) to (29), and the distribution thereof is shown in fig. 3.
Fig. 3 shows that 4667 common transformers in the area a in 2018 have poor power supply reliability, and the specific reasons of poor common transformer reliability can be accurately judged by checking the status scores of the individual items.
The method provided by the invention verifies the evaluation method by using the public transformation operation data of the area A, the result is matched with the actual public transformation reliability condition, and the power supply reliability state of each distribution network public transformation can be effectively reflected, so that the method is reasonable, effective and feasible in analysis and has popularization and application values.
Application example
The invention can be used as a functional module of a big data analysis platform, a computer program is compiled according to the principle and the flow chart of the invention, the computer program is deployed on an operation server of the big data analysis platform, the distribution transformer running state data collected by the electricity utilization information collection system in a certain period is analyzed, the reliability of the distribution transformer is automatically evaluated, and the specific reason of the poor reliability of the distribution transformer can be accurately judged.
As shown in fig. 4, the big data analysis platform obtains the operation data of all distribution transformers to be analyzed from the power consumption information collection system through a uniform interface program, then analyzes the operation data by a programmed computer program, screens out a distribution transformer list with poor reliability, stores the screening result in a database server of the data interaction platform, responds to the requests of power supply units of province, city, county and each level of the power supply unit by a WEB server of the data interaction platform, and displays the screening result to the monitoring terminals of the power supply units of the province, city, county and each level of the power supply unit.
Claims (10)
1. A comprehensive evaluation method for distribution transformer power supply reliability is characterized by comprising the following steps:
(1) acquiring distribution transformation archive data, load data, current data, voltage data, power factor data and fault power failure information data;
(2) calculating a distribution transformer fault power-off index and a distribution transformer fault risk index;
(3) processing the data obtained in the step (2);
(4) respectively calculating the weight coefficients of the distribution transformer fault power failure indexes and the distribution transformer fault risk indexes by using an AHP analytic hierarchy process;
(5) calculating the reliability evaluation value of the distribution transformer based on the distribution transformer fault outage class index and the distribution transformer fault risk class index by using a TOPSIS algorithm;
(6) and calculating a comprehensive evaluation value of the power supply reliability of the distribution transformer.
2. The comprehensive evaluation method for the power supply reliability of the distribution transformer according to claim 1, wherein in the step (1), data acquisition is performed on a certain distribution transformer type within a certain area according to a period to be analyzed from the power utilization information acquisition system.
3. The method according to claim 1, wherein the indicators of distribution transformer fault outage class in step (2) include the number of continuous blackouts, the number of short blackouts, the average duration blackout duration, and the average short blackout duration.
4. The comprehensive evaluation method for the power supply reliability of the distribution transformer according to claim 1, wherein the distribution transformer fault risk indicators comprise heavy load times, overload times, severe overload times, power factor abnormal times, three-phase unbalance times and low voltage times.
5. The comprehensive evaluation method for power supply reliability of distribution transformer according to claim 1, wherein the data processing of step (3) comprises setting the upper and minimum values of each feature and normalizing each index.
6. The comprehensive evaluation method for power supply reliability of distribution transformer according to claim 1, wherein the step (4) specifically comprises the following steps:
(A) respectively constructing a judgment matrix of a distribution transformer fault power failure index and a judgment matrix of a distribution transformer fault risk index;
(B) respectively carrying out consistency check on the two judgment matrixes;
(C) after consistency check is carried out on the two judgment matrixes, the weight coefficient of each index is calculated by respectively utilizing the eigenvector corresponding to the maximum characteristic root.
7. The comprehensive evaluation method for power supply reliability of distribution transformer according to claim 1, wherein the step (5) specifically comprises the following steps:
(I) respectively constructing a characteristic matrix of a distribution transformer fault power failure index and a characteristic matrix of a distribution transformer fault risk index;
(II) weighting the characteristic matrix of the distribution transformer fault power failure type index and the characteristic matrix of the distribution transformer fault risk type index respectively to obtain weighted characteristic matrices;
(III) finding the optimal solution vector and the worst solution vector of each column;
(IV) calculating Euclidean distances between each index and the optimal solution vector and the worst solution vector;
and (V) respectively calculating the grading values of the distribution transformer fault power-off index and the distribution transformer fault risk index.
8. The comprehensive evaluation method of the power supply reliability of the distribution transformer according to claim 7, wherein in the step (III), the value of each feature is zero as an optimal solution; the numerical upper limit of each feature is taken as the worst solution.
9. The comprehensive evaluation method for power supply reliability of distribution transformer according to claim 1, wherein the step (6) specifically comprises the following steps:
(a) calculating grading weights of the distribution transformer fault power failure indexes and the distribution transformer fault risk indexes by adopting a variable weight method;
(b) and weighting the score values and summing to obtain a distribution transformer power supply reliability comprehensive score value, wherein the smaller the score value is, the worse the distribution transformer power supply reliability is.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the method of any one of claims 1to 9.
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